**Speaker**: Harish Bhat (UC Merced)

**Title**: BCD-Prox Methods for Simultaneous Filtering & Parameter Estimation

**Description**: Suppose we have a dynamical system with parameters whose values we'd like to estimate using noisy observations of the system's state. This is the simultaneous filtering and parameter estimation problem -- here filtering is in the sense of the classical Kalman filter, where one seeks to estimate the system's true states from noisy observations. We describe our recent work on block coordinate descent proximal methods (BCD-prox) to solve this problem. As compared to state-of-the-art methods, BCD-prox exhibits increased robustness (to noise, parameter initialization, and hyperparameters), decreased training times, and improved accuracy of both filtered states and estimated parameters. We show how BCD-prox can be used with multistep numerical discretizations, and we establish convergence of BCD-prox under hypotheses that include real systems of interest.